Skip to main content
Phoenix helps you understand and improve AI applications by giving you a workflow for debugging and iteration. You can send detailed logging information, known as traces, from your app to see exactly what happened during a run, score outputs using evaluation tests to identify failures and regressions, iterate on your prompts using real production examples, and optimize your app with experiments that compare changes on the same inputs. Together, these tools help you move from inspecting individual runs to improving quality with evidence. Phoenix is built by Arize AI and the open-source community. It is built on top of OpenTelemetry and is powered by OpenInference instrumentation. See Integrations for details.

​
Features

Tracing in Phoenix

Tracing lets you see what happened during a single run of your AI application, step by step. A trace captures model calls, retrieval, tool use, and custom logic so you can debug behavior and understand where time is spent.Phoenix accepts traces over OpenTelemetry (OTLP) and provides auto-instrumentation for popular frameworks (LlamaIndex, LangChain, DSPy, Mastra, Vercel AI SDK), providers (OpenAI, Bedrock, Anthropic), and languages (Python, TypeScript, Java).

​
Quick Starts

Running Phoenix for the first time? Let your coding agent set this up. Works with Claude Code, Codex, Cursor, and OpenCode.
1

Start Phoenix

In one terminal, start Phoenix and leave it running. It serves at http://localhost:6006:
Already have a Phoenix deployment? Skip this step. px setup can point at any running Phoenix, including a self-hosted one.
2

Add tracing with a coding agent

In a new terminal, from your app’s root directory, run one of these:
px setup hands the instrumentation to your coding agent and waits until a real trace arrives. It can also install the Phoenix skills so your agent can query what you capture. See Agent-Assisted Setup for the full flow, CI usage, and unsupported agents.
Or pick a quick start below to wire it up yourself.

​
Python

Send Traces From Your App

See what’s happening inside your LLM application with distributed tracing

Measure Performance with Evaluations

Measure quality with LLM-as-a-judge and custom evaluators

Iterate on Your Prompts

Experiment with prompts, compare models, and version your work

Optimize Your App with Experiments

Test your application systematically and track performance over time

​
TypeScript

Send Traces From Your App

See what’s happening inside your LLM application with distributed tracing

Measure Performance with Evaluations

Measure quality with LLM-as-a-judge and custom evaluators

Iterate on Your Prompts

Experiment with prompts, compare models, and version your work

Optimize Your App with Experiments

Test your application systematically and track performance over time

​
Next Steps

The best next step is to start using Phoenix. Start with a quickstart to send data into Phoenix, then build from there. See the Quickstart Overview for more information about what you’ll build.

​
Other Resources

Built-in Agent (PXI)

Use PXI, the agent built into Phoenix, to debug traces and iterate on prompts in context

Integrations

Add instrumentation for OpenAI, LangChain, LlamaIndex, and more

Self-Host

Deploy Phoenix on Docker, Kubernetes, or your cloud of choice

Cookbooks

Example notebooks for tracing, evals, RAG analysis, and more

Community

Join the Phoenix Slack to ask questions and connect with developers